Titlebook: Deep Learning for Power System Applications; Case Studies Linking Fangxing Li,Yan Du Book 2024 The Editor(s) (if applicable) and The Author

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書目名稱Deep Learning for Power System Applications影響因子(影響力)




書目名稱Deep Learning for Power System Applications影響因子(影響力)學科排名




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書目名稱Deep Learning for Power System Applications網(wǎng)絡公開度學科排名




書目名稱Deep Learning for Power System Applications被引頻次




書目名稱Deep Learning for Power System Applications被引頻次學科排名




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書目名稱Deep Learning for Power System Applications年度引用學科排名




書目名稱Deep Learning for Power System Applications讀者反饋




書目名稱Deep Learning for Power System Applications讀者反饋學科排名

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Book 2024 well as practicing engineers and AI researchers..Provides a history of AI in power grid operation and planning;.Introduces deep learning algorithms and applications in power systems;.Includes several representative case studies..

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Book 2024resentative case studies of deep learning techniques in power systems are investigated and discussed, including convolutional neural networks (CNN) for power system security screening and cascading failure assessment, deep neural networks (DNN) for demand response management, and deep reinforcement

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Desistance from Sexual Offendingpplying the proposed deep CNN and the DFS algorithm on standard test cases verify their accuracy and that their computational efficiency is thousands of times faster than the model-based traditional approach, which implies the great potential of the proposed algorithm for online applications.

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Deep Neural Network for Microgrid Management,, which optimizes the decision based on prediction. The simulation results from the proposed data-driven deep learning method, as well as comparisons with conventional model-based methods, substantiate the effectiveness of the proposed approach in solving power system problems with partial or uncertain information.

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